Agentic FinOps: An Autonomous Framework for Real-Time Cost Governance in AI-Infused Cloud Architectures

Abstract

As enterprises scale Generative AI from experimental pilots to mission critical production, cloud expenditures have shifted toward volatile, token based and GPU intensive consumption models. Traditional reactive FinOps frameworks fail to address the nonlinear cost dynamics of large language models (LLMs) and distributed AI workloads. This paper introduces Agentic FinOps, an autonomous governance framework that leverages multi-agent AI systems to move beyond passive cost reporting toward proactive real time execution. Agents embedded in the cloud orchestration layer enable autonomous resource right sizing, predictive budget enforcement via token quota management, and cross cloud GPU optimization. We propose a novel Unit Cost of Inference (UCI) metric that aligns AI engineering decisions with business value, and an Autonomous Token-Quota Balancing (ATQB) algorithm enforces hard budget limits while preserving Service Level Objectives (SLOs). A reference Multi-Agent Governance (MAG) architecture demonstrates how autonomous interventions reduce waste and maintain resilience. SLO-aware execution controls constrain cost interventions within an application stability envelope, making the framework suitable for latency-sensitive distributed environments.